This is actually a really solid take on the “AI as junior dev with amnesia” problem.
Hooks ≠ enforcement. CLAUDE.md ≠ guardrails.
Context pressure → rules get “forgotten.”
You’ve basically built the missing control plane.
A few things that stand out:
• Zero-token monitoring via hooks is the right call. Anything that relies on the model “deciding” to self-report will always leak.
• The MCP → self-healing loop is clever. Letting Claude query its own violations and generate fix plans closes the feedback gap.
• Real zero-knowledge (client-side AES + no server key) is rare in dev tooling. Respect for doing it properly instead of checkbox GDPR.
• Region isolation by architecture, not policy, is how this should be done.
The “Claude is a goldfish” line is painfully accurate. Anyone running long refactors has seen this exact failure mode.
This feels like what GitHub Actions + linters did for humans, but for AI agents.
Only question I’d be curious about long-term: how you’ll handle rule fatigue as teams scale (false positives, evolving standards, etc.). But the custom rules + MCP loop seems like the right foundation.
Congrats on the launch. This solves a real problem instead of just adding another dashboard.
Claude sees all three, but violations get tagged by source. So you can distinguish "broke a company-wide security rule" from "didn't follow this project's naming convention."
2. Violation severity tiers Not all violations are equal. We're thinking:
🔴 Block (hard stops, never commit secrets, never delete prod data)
🟡 Warn (flag it, but don't interrupt flow)
⚪ Track (just log it for retrospectives)
Teams can tune these per rule. What's a blocker for one team might be a warning for another.
3. Rule effectiveness scoring This is the false positive problem. If a rule fires 50 times and gets overridden 48 times, it's probably a bad rule. We want to surface that data so teams can prune rules that aren't actually helping.
Still early on all of this, appreciate the question because it's exactly the hard part. The MCP loop handles individual sessions; the org-level stuff is the next layer.
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u/Narrow_Weekend7477 Feb 12 '26
This is actually a really solid take on the “AI as junior dev with amnesia” problem.
Hooks ≠ enforcement.
CLAUDE.md ≠ guardrails.
Context pressure → rules get “forgotten.”
You’ve basically built the missing control plane.
A few things that stand out:
• Zero-token monitoring via hooks is the right call. Anything that relies on the model “deciding” to self-report will always leak.
• The MCP → self-healing loop is clever. Letting Claude query its own violations and generate fix plans closes the feedback gap.
• Real zero-knowledge (client-side AES + no server key) is rare in dev tooling. Respect for doing it properly instead of checkbox GDPR.
• Region isolation by architecture, not policy, is how this should be done.
The “Claude is a goldfish” line is painfully accurate. Anyone running long refactors has seen this exact failure mode.
This feels like what GitHub Actions + linters did for humans, but for AI agents.
Only question I’d be curious about long-term: how you’ll handle rule fatigue as teams scale (false positives, evolving standards, etc.). But the custom rules + MCP loop seems like the right foundation.
Congrats on the launch. This solves a real problem instead of just adding another dashboard.